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Personal profile

Research interests

Beate’s research interests cover a wide variety of data analysis methods, from hypothesis testing, Bayesian statistics to machine learning, and optimal experimental design. Beate has experience working with pharmaceutical data, insurance data, and data in the social sciences.

Before joining the IMI, Beate worked as a Senior Research Statistician on pre-clinical data at AstraZeneca — a large pharmaceutical company. As part of Discovery Sciences, she designed and analysed experiments across the pre-clinical pipeline — from hit identification to animal experiments.

Her background is in mathematical statistics in which she completed a PhD at University College London (UCL) at the Department for Statistical Science. In her PhD, Beate worked on improving our understanding of community structure in large networks.

Education/Academic qualification

Statistical Sciences, Doctor of Philosophy, University College London

30 Sep 201230 Sep 2016

Mathematics, Master of Science, Universität Bremen

30 Sep 200615 Jun 2012

Keywords

  • Hypothesis testing
  • Bayesian statistics
  • Machine Learning
  • Networks
  • Optimal experimental design
  • Causality

Fingerprint Dive into the research topics where Beate Ehrhardt is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

drug Earth & Environmental Sciences
hypothesis testing Earth & Environmental Sciences
experimental design Earth & Environmental Sciences
community structure Earth & Environmental Sciences
experiment Earth & Environmental Sciences
statistics Earth & Environmental Sciences
science Earth & Environmental Sciences
family Earth & Environmental Sciences

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2019 2019

Research Output 2016 2019

An innovative non-invasive technique for subcutaneous tumour measurements

Martin, J. D. S., Ehrhardt, B., Paczkowski, M., Hackett, S., Smith, A., Waraich, W., Klatzow, J., Zabair, A., Chabokdast, A., Rubio-Navarro, L., Rahi, A. & Wilson, Z., 14 Oct 2019, In : PLoS ONE. 14, 10, p. 1-14 14 p., e0216690.

Research output: Contribution to journalArticle

Open Access
8 Citations (Scopus)

Introducing an automated high content confocal imaging approach for Organs-on-Chips

Peel, S., Corrigan, A. M., Ehrhardt, B., Jang, K-J., Caetano-Pinto, P., Boeckeler, M., Rubins, J. E., Kodella, K., Petropolis, D. B., Ronxhi, J., Kulkarni, G., Foster, A. J., Williams, D., Hamilton, G. A. & Ewart, L., 29 Jan 2019, In : Lab on a Chip. 19, 3, p. 410-421 12 p.

Research output: Contribution to journalArticle

20 Downloads (Pure)

Network modularity in the presence of covariates

Ehrhardt, B. & Wolfe, P. J., 2019, In : Siam Review. 61, 2, p. 261-276 16 p.

Research output: Contribution to journalReview article

Open Access
1 Citation (Scopus)

Non-locking screw insertion: No benefit seen if tightness exceeds 80% of the maximum torque

Fletcher, J. W. A., Ehrhardt, B., MacLeod, A., Whitehouse, M. R., Gill, H. & Preatoni, E., 1 Dec 2019, In : Clinical Biomechanics. 70, p. 40-45 6 p.

Research output: Contribution to journalArticle

15 Citations (Scopus)

Statistical inference, learning and models in big data

Franke, B., Plante, JF., Roscher, R., Lee, E-S. A., Smyth, C., Hatefi, A., Chen, F., Gil, E., Schwing, A., Srlvitella, A., Hoffman, M. M., Grosse, R., Hendricks, D. & Reid, N., 1 Dec 2016, In : International Statistical Review. 84, 3, p. 371-389 84.

Research output: Contribution to journalArticle

Open Access

Datasets

Dataset for "Non-locking screw insertion: no benefit seen if tightness exceeds 80% of the maximum torque"

Fletcher, J. (Creator), Ehrhardt, B. (Creator), MacLeod, A. (Creator), Whitehouse, M. R. (Creator), Gill, R. (Creator), Preatoni, E. (Creator), University of Bath, 9 Jul 2019

Dataset